Many different systems have been developed for the detection and extraction of commercials from broadcast or recorded video signals. For example, U.S. Pat. No. 4,782,401 entitled “Editing Method and Apparatus for Commercials During Video Recording” describes a hardware-oriented solution for editing out commercials in the analog domain, based on the presence of dark or blank frames used to delineate commercials.
A similar system is described in PCT Application No. WO 83/00971, entitled “Reciprocating Recording Method and Apparatus for Editing Commercial Messages from Television Signals.” This system edits out commercials based on fade-in and fade-out at the beginning and end, respectively, of a commercial break.
Another approach, described in U.S. Pat. No. 4,750,052 entitled “Apparatus and Method for Deleting Selected Program Intervals from Recorded Television Broadcasts,” utilizes a fade detector to edit commercials from a recorded broadcast program.
PCT Application No. WO 94/27404, entitled “Method and Apparatus for Classifying Patterns of Television Programs and Commercials,” uses feature extraction and a neural network to classify video signals. The system detects changes in features such as power amplitude over the frequency spectrum, color and brightness, vertical interval time code, closed caption signal, and color carrier jitter signal.
A system described in PCT Application No. WO 95/06985, entitled “Process and Device for Detecting Undesirable Video Scenes,” stores an image from a broadcast program that precedes a commercial break so that the end of the commercial break may be detected by means of comparing a current image to the stored image. This approach makes use of the fact that broadcasters often repeat a small part of the program after the end of the commercial break.
European Patent Application No. EP 735754, entitled “Method and Apparatus for the Classification of Television Signals,” uses a set of features and associated rules to determine if the current commercials satisfy the same criteria with some degree of “fuzziness.” The set of features includes, e.g., stereo versus mono, two-channel audio, sound level, image brightness and color, and logos, used to characterize commercials. An extensive set of rules is required to accommodate thresholds and parameter variations for these features.
U.S. Pat. No. 5,708,477, entitled “Video Signal Identifier for Controlling a VCR and Television Based on the Occurrence of Commercials,” uses a video signal identifier to recognize previously-identified commercial material and to reject it either by muting the television sound and/or pausing the VCR when it is in record mode. A significant problem with this approach is that it fails to provide automatic detection, i.e., it requires the material to be identified in some way prior to its detection.
A system described in U.S. Pat. No. 5,668,917, entitled “Apparatus and Method for Detection of Unwanted Broadcast Information,” uses the repetitiveness of commercials to identify commercial material. This system stores video frames in a compressed format and compares frames in original “raw” format pixel by pixel. If the pixels match, within some threshold, then the frames are considered similar. A serious drawback of this approach is the excessive memory and computational resources that it requires. More particularly, storing video even in a compressed format takes an impractically large amount of memory space, e.g., approximately 200 GB per day for one channel of high definition television (HDTV) content. In addition, comparing raw video is very time consuming. Even assuming that compressing and decompressing video can be implemented at no additional computational cost, comparing frames will be a very slow process. A given incoming frame must be compared with the above-noted large amounts of stored video material, and the comparison completed before the next frame arrives.
The techniques described in the above-cited U.S. patent application Ser. No. 09/417,288 provide substantial improvements over the conventional approaches outlined above, through the use of signature-based spotting, learning and extraction. However, despite the substantial improvements provided by these techniques, a need nonetheless remains for further improvements in the identification and extraction of commercials and other types of video content.